Forming regions characterized by labeled measurements

ABSTRACT

Briefly, embodiments of methods and/or apparatuses for processing, at a variety of scale levels, labeled measurements in sub-regions to form a region characterized by a set of labeled measurements is described.

CROSS-REFERENCE TO RELATED APPLICATIONS

Although claimed subject matter is not necessarily limited in scope inthis respect, additional example embodiments of FORMING REGIONSCHARACTERIZED BY LABELED MEASUREMENTS may be discussed in concurrentlyfiled U.S. patent application Ser. No. ______, titled IDENTIFYINGREGIONS CHARACTERIZED BY LABELED MEASUREMENTS, by Bart Thomee, et al,herein incorporated by reference in its entirety and assigned to theassignee of currently claimed subject matter Attorney Docket No.070.P275 (Y08630US00) and in concurrently filed U.S. patent applicationSer. No. ______, titled VISUALIZING REGIONS CHARACTERIZED BY LABELEDMEASUREMENTS by Bart Thomee, et al. herein incorporated by reference inits entirety and assigned to the assignee of currently claimed subjectmatter Attorney Docket No. 070.P276 (Y08658US00).

BACKGROUND

1. Field

This disclosure relates to processing labeled measurements, at varyingscales, to form regions.

2. Information

In many applications, it may be useful to use computer-assisted graphicsto display measurements in a manner that may be more easily interpretedby, for example, researchers, scientists, investigators, students,and/or others. For example, a medical researcher investigating spread ofan illness or disease across a region, may find that computer-assistedimages representing geographical distribution of an illness or diseasemay be a useful tool in determining locations at which disease-fightingresources may be positioned. Such positioning may facilitate, forexample, more efficient and/or more effective deployment of healthcareassets in a manner that may reduce incidence of cases of affliction aswell as increase an ability to treat those already afflicted.

In another example in which display of computer-assisted graphics may beuseful, a social sciences researcher may study measurements of labelsassociated with photographs in a database in an effort to study regionaland/or national trends. In one possible example, trends among certainage groups may be evaluated by studying types of photographs taken byindividuals of certain age groups and/or locations at which photographicimages are captured, for example. This research may be used, forexample, to uncover factors contributing to trends so that, for example,ramifications of elections, political and/or economic decisions, etc.,may be better understood. To facilitate investigation,computer-generated graphics, for example, may be used to assist aresearcher in visualizing trends and/or assessing how trends maypropagate within a society, region, nation, etc.

Unfortunately, contemporary computer visualization tool sets may not becapable of assisting in epidemiological and/or social sciencesinvestigations, for example. Thus, various types of epidemiology and/orsocial science research may be at least partially hindered by aninability to comprehensively and/or accurately visualize variousphenomena. Researchers, for example, may be constrained to usingless-effective and/or manual tools, which may be slow and/or cumbersometo manipulate. Further, communication of results of investigations to anaudience, perhaps by way of computer-assisted imagery, may be lesseffective or even impaired. This may, for example, potentially reduce abenefit of conducting the investigation.

BRIEF DESCRIPTION OF DRAWINGS

Claimed subject matter is particularly pointed out and distinctlyclaimed in the concluding portion of the specification. However, both asto organization and/or method of operation, together with objects,features, and/or advantages thereof, claimed subject matter may beunderstood by reference to the following detailed description if readwith the accompanying drawings in which:

FIGS. 1A-1C are diagrams depicting regions within a geographical spacecharacterized by labeled measurements according to an embodiment;

FIG. 2 is a flow diagram of a process for forming one or more regionscharacterized by labeled measurements according to an embodiment;

FIGS. 3A and 3B show a joining and infilling operation that may be usedto form one or more regions characterized by labeled measurementsaccording to an embodiment;

FIGS. 4A and 4B show additional image processing that may be used toform one or more identified regions according to an embodiment; and

FIG. 5 is a schematic diagram of a system that may be employed forforming and displaying one or more regions characterized by labeledmeasurements according to an embodiment.

Reference is made in the following detailed description to accompanyingdrawings, which form a part hereof, wherein like numerals may designatelike parts throughout to indicate corresponding and/or analogouscomponents. It will be appreciated that components illustrated in thefigures are not necessarily drawn to scale, such as for simplicityand/or clarity of illustration. For example, dimensions of somecomponents may be exaggerated relative to other components. Further, itis to be understood that other embodiments may be utilized. Furthermore,structural and/or other changes may be made without departing fromclaimed subject matter. It should also be noted that directions and/orreferences, for example, up, down, top, bottom, and so on, may be usedto facilitate discussion of drawings and/or are not intended to restrictapplication of claimed subject matter. Therefore, the following detaileddescription is not to be taken to limit claimed subject matter and/orequivalents.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth to provide a thorough understanding of claimed subject matter. Forpurposes of explanation, specific numbers, systems and/or configurationsare set forth, for example. However, it should be apparent to oneskilled in the relevant art having benefit of this disclosure thatclaimed subject matter may be practiced without specific details. Inother instances, well-known features may be omitted and/or simplified soas not to obscure claimed subject matter. While certain features havebeen illustrated and/or described herein, many modifications,substitutions, changes and/or equivalents may occur to those skilled inthe art. It is, therefore, to be understood that appended claims areintended to cover any and all modifications and/or changes as fallwithin claimed subject matter.

Reference throughout this specification to one implementation, animplementation, one embodiment, an embodiment and/or the like may meanthat a particular feature, structure, or characteristic described inconnection with a particular implementation or embodiment may beincluded in at least one implementation or embodiment of claimed subjectmatter. Thus, appearances of such phrases, for example, in variousplaces throughout this specification are not necessarily intended torefer to the same implementation or to any one particular implementationdescribed. Furthermore, it is to be understood that particular features,structures, or characteristics described may be combined in various waysin one or more implementations. In general, of course, these and otherissues may vary with context. Therefore, particular context ofdescription or usage may provide helpful guidance regarding inferencesto be drawn.

Operations and/or processing, such as in association with networks, suchas communication networks, for example, may involve physicalmanipulations of physical quantities. Typically, although notnecessarily, these quantities may take the form of electrical and/ormagnetic signals capable of, for example, being stored, transferred,combined, processed, compared and/or otherwise manipulated. It hasproven convenient, at times, principally for reasons of common usage, torefer to these signals as bits, data, values, elements, symbols,characters, terms, numbers, numerals and/or the like. It should beunderstood, however, that all of these or similar terms are to beassociated with appropriate physical quantities and are intended tomerely be convenient labels.

Likewise, in this context, the terms “coupled”, “connected,” and/orsimilar terms, may be used. It should be understood that these terms arenot intended as synonyms. Rather, “connected” may be used to indicatethat two or more elements or other components, for example, are indirect physical and/or electrical contact; while, “coupled” may meanthat two or more components are in direct physical or electricalcontact; however, “coupled” may also mean that two or more componentsare not in direct contact, but may nonetheless co-operate or interact.The term coupled may also be understood to mean indirectly connected,for example, in an appropriate context.

The terms, “and”, “or”, “and/or” and/or similar terms, as used herein,may include a variety of meanings that also are expected to depend atleast in part upon the particular context in which such terms are used.Typically, “or” if used to associate a list, such as A, B or C, isintended to mean A, B, and C, here used in the inclusive sense, as wellas A, B or C, here used in the exclusive sense. In addition, the term“one or more” and/or similar terms may be used to describe any feature,structure, and/or characteristic in the singular and/or may be used todescribe a plurality or some other combination of features, structuresand/or characteristics. However, it should be noted that this is merelyan illustrative example and claimed subject matter is not limited tothis example. Again, particular context of description or usage mayprovide helpful guidance regarding inferences to be drawn.

It should be understood that for ease of description a network devicemay be embodied and/or described in terms of a computing device.However, it should further be understood that this description should inno way be construed that claimed subject matter is limited to oneembodiment, such as a computing device or a network device, and,instead, may be embodied as a variety of devices or combinationsthereof, including, for example, one or more illustrative examples.

In this context, the term network device refers to any device capable ofcommunicating via and/or as part of a network. Network devices may becapable of sending and/or receiving signals (e.g., signal packets), suchas via a wired or wireless network, may be capable of performingarithmetic and/or logic operations, processing and/or storing signals,such as in memory as physical memory states, and/or may, for example,operate as a server. Network devices capable of operating as a server,or otherwise, may include, as examples, dedicated rack-mounted servers,desktop computers, laptop computers, set top boxes, tablets, netbooks,smart phones, integrated devices combining two or more features of theforegoing devices, the like or any combination thereof.

A network may comprise two or more network devices and/or may couplenetwork devices so that signal communications, such as in the form ofsignal packets, for example, may be exchanged, such as between a serverand a client device and/or other types of network devices, includingbetween wireless devices coupled via a wireless network, for example. Itis noted that the terms, server, server device, server computing device,server computing platform, and/or similar terms are usedinterchangeably. Similarly, the terms client, client device, clientcomputing device, client computing platform and/or similar terms arealso used interchangeably. While in some instances, for ease ofdescription, these terms may be used in the singular, such as byreferring to a “client device” or a “server device,” the description isintended to encompass one or more client devices or one or more serverdevices, as appropriate. Along similar lines, references to a “database”are understood to mean, one or more databases and/or portions thereof,as appropriate.

A network may also include now known, or to be later developedarrangements, derivatives, and/or improvements, including, for example,past, present and/or future mass storage, such as network attachedstorage (NAS), a storage area network (SAN), and/or other forms ofcomputer and/or machine readable media, for example. A network mayinclude the Internet, one or more local area networks (LANs), one ormore wide area networks (WANs), wire-line type connections, wirelesstype connections, other connections, and/or any combination thereof.Thus, a network may be worldwide in scope and/or extent. Likewise,sub-networks, such as may employ differing architectures or may becompliant and/or compatible with differing protocols, such ascommunication protocols (e.g., network communication protocols), mayinteroperate within a larger network. Various types of devices may bemade available so that device interoperability is enabled and/or, in atleast some instances, may be transparent to the devices. In thiscontext, the term transparent refers to communicating in a manner sothat communications may pass through intermediaries, but without thecommunications necessarily specifying one or more intermediaries, suchas intermediate devices, and/or may include communicating as ifintermediaries, such as intermediate devices, are not necessarilyinvolved. For example, a router may provide a link between otherwiseseparate and/or independent LANs. In this context, a private networkrefers to a particular, limited set of network devices able tocommunicate with other network devices in the particular, limited set,such as via signal packet transmissions, for example, without a need forre-routing and/or redirecting such communications. A private network maycomprise a stand-alone network; however, a private network may alsocomprise a subset of a larger network, such as, for example, withoutlimitation, the Internet. Thus, for example, a private network “in thecloud” may refer to a private network that comprises a subset of theInternet, for example. Although signal packet transmissions may employintermediate devices to exchange signal packet transmissions, thoseintermediate devices may not necessarily be included in the privatenetwork by not being a source or destination for one or more signalpacket transmissions, for example. As another example, a logicalbroadcast domain may comprise an example of a private network. It isunderstood in this context that a private network may provide outgoingcommunications to devices not in the private network, but such devicesoutside the private network may not direct inbound communications todevices included in the private network.

The Internet refers to a decentralized global network of interoperablenetworks, including devices that are part of those interoperablenetworks. The Internet includes local area networks (LANs), wide areanetworks (WANs), wireless networks, and/or long haul public networksthat, for example, may allow signal packets to be communicated betweenLANs. The term worldwide web (WWW) and/or similar terms may also be usedto refer to the Internet. Signal packets, also referred to as signalpacket transmissions, may be communicated between nodes of a network,where a node may comprise one or more network devices, for example. Asan illustrative example, but without limitation, a node may comprise oneor more sites employing a local network address. Likewise, a device,such as a network device, may be associated with that node. A signalpacket may, for example, be communicated via a communication channel ora communication path comprising the Internet, from a site via an accessnode coupled to the Internet. Likewise, a signal packet may be forwardedvia network nodes to a target site coupled to a local network, forexample. A signal packet communicated via the Internet, for example, maybe routed via a path comprising one or more gateways, servers, etc. thatmay, for example, route a signal packet in accordance with a targetaddress and availability of a network path of network nodes to a targetaddress.

Physically connecting portions of a network via a hardware bridge, asone example, may be done, although other approaches also exist. Ahardware bridge, however, may not typically include a capability ofinteroperability via higher levels of a network protocol. A networkprotocol refers to a set of signaling conventions for communicationsbetween or among devices in a network, typically network devices, butmay include computing devices, as previously discussed; for example,devices that substantially comply with the protocol or that aresubstantially compatible with the protocol. In this context, the term“between” and/or similar terms are understood to include “among” ifappropriate for the particular usage. Likewise, in this context, theterms “compatible with,” “comply with” and/or similar terms areunderstood to include substantial compliance or substantialcompatibility. Typically, a network protocol has several layers. Theselayers may be referred to here as a communication stack. Various typesof communications may occur across various layers. For example, as onemoves higher in a communication stack, additional functions may beavailable by transmitting communications that are compatible and/orcompliant with a particular network protocol at these higher layers.

In contrast, a virtual private network (VPN) may enable a remote deviceto communicate via a local network. A router may allow communications inthe form of transmissions (e.g., signal packets), for example, to occurfrom a remote device to a VPN server on a local network. A remote devicemay be authenticated and a VPN server, for example, may create a specialroute between a local network and the remote device through anintervening router.

A network may be very large, such as comprising thousands of nodes,millions of nodes, billions of nodes, or more, as examples. Medianetworks, such as the Yahoo!™ network, for example, are increasinglyseeking ways to keep users within their networks by providing featuresand/or tool sets that users may find useful. A media network may, forexample, comprise an Internet website or group of websites having one ormore sections appealing to different interests or aspects of a user'sexperience, for example. For instance, the Yahoo!™ network includeswebsites located within different categorized sections, such as sports,finance, photo sharing (e.g., Flickr™) current events, and/or games, toname just a few among possible non-limiting examples.

The more users remaining within a media network for an extended period,the more valuable a network may become to potential advertisers. Thus,the more money advertisers may be inclined to pay to advertise to users,for example, via that media network. In an implementation, for example,an online photo sharing site, such as the Yahoo!™ Flickr™ service, mayenable a user to share photographs among a vast community of contactsand/or other users, via a server or other type of computing platform. Inparticular implementations, photographic assets, such as pictures, videoclips, multimedia clips, or other images, may be associated with one ormore labels, such as assigned by a user or otherwise assigned, so thatother users of an online service may access an image or otherphotographic asset of potential interest Use of online photo sharingservices may enable a user to keep up with friends and/or acquaintances,explore portions of the world with which they may be unfamiliar, and/orinvestigate localized areas, regions, and/or entire countries for futuretravel, just to name a few non-limiting examples. For example, if a useris interested in traveling to Italy, a user may search an online photosharing service for images having a label, such as “Italy,” or may beassociated with locations, such as within the Italian peninsula.Although claimed subject matter is, of course, not limited in thisrespect, an implementation, such as online photo sharing services, mayentice users to remain within a network for a relatively extendedperiod.

In an implementation, a media network may serve as a collection pointfor “labeled measurements” that may be associated with one or morelocations, such as a geographical location, for example. However,labeled measurements need not be restricted to points that representphysical locations on the surface of the Earth, for example. In otherimplementations, a measurement may be associated with a non-spatialphysical manifestation, that may, for example, be represented as one ormore coordinate points, such as in a multidimensional vector space, asignal space, or another domain that may be employed to represent one ormore features of a measureable physical manifestation. For example, afrequency domain or a wavelength domain, as simply one example, may beemployed. Within a given space, points, lines, and/or surfaces may berepresented using a linear combination of a set of mutually orthogonalvectors that span the space, although, again, this is simply anon-limiting example. It should be noted that claimed subject matter isintended to embrace all domains capable of representing a measureablephysical manifestation, whether in two dimensions, three dimensions,additional dimensions and/or other domain features, such as non-linearand/or non-spatial characteristics.

In an implementation, as another example, a labeled measurement maycomprise one or more physical states (e.g., memory states) to encodelight levels of various wavelengths in an image capture device (e.g.,camera). Thus, for example, a labeled measurement may represent an imagecaptured and stored as a JPEG, TIFF, or other type of physical state andmay be associated with an approximate location on the Earth at which animage was captured. Accordingly, a labeled measurement may include aphotograph, video clip, and/or other multimedia segment, just to name afew examples. Of course, again, claimed subject matter is not limited toillustrative examples.

In another implementation example, a labeled measurement may comprise anarrangement of one or more physical states (e.g., memory states) toencode, for example, a date and/or a location of a meeting, a dateand/or location of a social event, such as a parade, a party, or otherfestivity, etc. A labeled measurement may comprise a report of adiscrete event, such as a measurement of a snow level at a location at acertain time or a location and time of an incidence of a disease orillness, just to name a few examples. A labeled measurement may beassociated with a location larger than a single point, such as arelatively small area, such as 1.0 square kilometer or less, or may beassociated with events distributed over a much larger region, such as acountry, continent, or other landmass. Again, claimed subject matter isnot limited to illustrative example. Thus, a labeled measurement mayalso represent a summation or other aggregation of measurements, suchas, for example, an indication of snowfall measurements extracted fromvarious points located on mountain, an entire mountain resort, or acrossa mountainous region. Accordingly, claimed subject matter is intended toembrace identifying labeled measurements comprising any number ofarrangements of physical states, for example, that may encodemeasurements that may be useful and/or of interest.

According to one or more implementations, as discussed herein,measurements may be assigned a label corresponding to a name of a city,such as San Francisco, Los Angeles, Madrid, or the like. A label mayalso comprise a name of a landmark or an establishment, such as theGolden Gate Bridge, Fenway Park, and so forth; a name of a naturallyoccurring feature, such as the Grand Canyon, Yellowstone National Park,and so forth; or may include any other identifier. Labels may alsocomprise any other descriptive references, such as clouds, sunsets,beaches, parks, countries, names of individuals or groups, and/or alarge variety of other descriptors that may be useful in categorizing alabeled measurement. It should be noted that claimed subject matter iscontemplated as embracing all types of labels that may be assigned tomeasurements and all types of physical locations or abstracted locationswithin mathematical or vector spaces associated with such measurements,for example.

In an implementation, a computing device, for example, may receiveand/or access labeled measurements stored in memory. In certainimplementations, a computing device, for example, may construct atwo-dimensional histogram comprising a number of bins, which maycorrespond to, for example, an area encompassed by 0.01° latitude and/or0.01° longitude on the surface of the Earth. Accordingly, for incrementsof approximately 0.01 degrees latitude, 36,000 discrete increments arepossible (360°/0.01°=36,000). Additionally, for step sizes ofapproximately 0.01 degrees longitude, 18,000 discrete steps are possible(180°/0.01°=18,000). In other implementations, smaller discretizationsof latitude and/or longitude other than 0.01° are possible as well aslarger discretizations, such as 0.02°. Again, these are illustrationsand claimed subject matter is not intended to be limited toillustrations. Therefore, other discretizations may be employed. Thus,in one possible example, of which many examples are possible, labeledmeasurements of a particular bin may be assigned or associated withcorresponding label(s) nearest (e.g., within) 0.01° latitude and/or0.01° longitude on the surface of the Earth. In other possible examples,discretizations pertaining to non-georeferenced labeled measurement binsmay correspond, for example, to dimensions measured in nanometers,kilometers, miles, acres, and so forth. Non-georeferenced labeledmeasurements may correspond to dimensions measured in light years, forexample, for extraterrestrial measurements. Discretizations may pertainto a number of pixels in an image, for example. Discretizations maypertain to un-evenly spaced dimensions at least partially resulting fromnon-linear conversions from labeled measurements (e.g. self-organizingfeature maps trained using unsupervised learning). Again, theserepresent only a few examples of a myriad of possible examples, andclaimed subject matter is not limited in this respect.

In an implementation, a representation of at least one identified regionencompassing locations of measured labels may be exhibited using one ormore “layers” on a display. It is noted that the term layer may beassociated with a particular scale level out of a variety of possiblescale levels. At a given layer, which may represent, for example, arelatively high-resolution representation of a portion of the surface ofthe earth, a region may be identified by detecting one or more outermostiso-contours that may bound an area within which prominence and/orconformance of label measurements relative to other label measurementsmay drop below a threshold level, for example. In this context, the termprominence refers to a characteristic in which some label measurementsmay tend to stand out among a larger set of label measurements. In someimplementations, computing prominence may include using a weightingfunction with labeled measurements to enhance a contribution of certainlabeled measurements in comparison other labeled measurements Likewise,in this context, the term conformance refers to a characteristic inwhich some label measurements may tend to be consistent within a largerset of label measurements. To display an identified region covering alarger area, for example, representing a portion of the surface of theEarth at somewhat lower relative resolution, e.g., another layer ordifferent relative scale level, a computing device may generate a secondhistogram. A second histogram may, at least in part, permit a user toincrease or to decrease a zoom level in a reasonably consistent mannerbetween a relatively lower resolution layer (e.g., relatively higherscale level) encompassing a relatively large identified region and arelatively higher resolution layer (e.g., relatively lower scale level)encompassing a relatively smaller identified region, for example.

In an implementation, a second layer may be generated using a smoothingprocess, such as with respect to a previously constructed histogram. Asmoothing process may comprise, at least in some implementations, aconvolution of a two-dimensional Gaussian kernel with a previouslyconstructed histogram, although other smoothing approaches may beemployed. Again, claimed subject matter is not limited to illustrations.

In certain implementations, a set of labeled measurements at a varietyof scale levels may be smoothed in a manner that may be at leastapproximately consistent with measurement labels across a variety ofscale levels. For example, at a relatively large scale, if a mapcorresponding to the European continent is displayed, one or morehistograms used to form regions that may be identified by way of“country-level” features, such as where Spanish or German citizens arecurrently located, may be smoothed using a particularly sized kernelemployed to convert a histogram to a sub-region. Smoothed histogramsused to form sub-regions may be joined or aggregated to form largerregions characterized by a labeled measurement. At various levels ofscale, which may vary, for example, from relatively high-resolution(e.g., city and/or town level) to relatively low-resolution (e.g.,country and/or continent level), sub-regions may be processed using anapproximately similar Gaussian kernel. It should be noted that claimedsubject matter is not limited to use of Gaussian smoothing, as otherimage processing techniques may be employed, such as Savitsky-Golay, forexample.

In particular embodiments, a change of scale, such as from viewing aregion at a relatively high-resolution to viewing a region at arelatively lower resolution, may involve joining adjacent histogramcells. In one possible example, as described further herein, bins of ahistogram may represent groupings of labeled measurements at a size ofinterest of 0.01° in longitude and latitude, for example. Adjacenthistogram bins may be combined into histogram bins representing, forexample, a larger size of interest, such as 0.02° in longitude andlatitude. Additional combining of histogram bins may be performed asrelative scale increases, and relative resolution decreases, such as bycombining histogram bins of 0.02° in longitude and latitude intohistogram bins of 0.04° in longitude and latitude, for example. Incertain embodiments, dividing regions characterized by labeledmeasurements into sub-regions and, for example, performing parallelimage processing on sub-regions may relatively decrease computationalcomplexity in forming boundaries of regions characterized by labeledmeasurements. Additionally, by combining histogram bins of sub-regionsas relative scale increases, computational complexity in formingboundaries of regions characterized by labeled measurements may berelatively decreased. Further, use of approximately identical smoothingkernels, such as a similarly sized Gaussian kernel, may furtherrelatively decrease computational complexity in performingimage-processing operations at varying scale levels.

In possible examples, at a relatively high scale level, such as ifdisplaying a map of the European continent, high-level regions may beformed, and potentially displayed, such as for locations within whichclusters of particular ethnic groups may reside. At a somewhat smallerrelative scale, encompassing, for example, a single country, (e.g.,Spain), histograms used to create regions that may be identified byareas within a country, such as the Spanish regions of Andalusia,Extremadura, or Valencia may be smoothed using a somewhat relativelysmaller Gaussian kernel, for example. At still other scale levels, suchas at a city or town level, histograms used to identify regions that maybe characterized by labels corresponding to a town square, a mall, orother location within a town may be smoothed using a somewhat relativelysmaller Gaussian kernel, for example, and displayed. Claimed subjectmatter is intended to embrace all instances in which labels associatedwith measurements may be smoothed in a manner appropriate or reasonablyconsistent with scale levels on a relative basis. In this context, assuggested previously, a relatively high resolution refers to arelatively lower scale and a relatively lower resolution refers to arelatively higher scale. In general, throughout this document it isunderstood that with respect to scale and/or resolution use of termssuch as high, low or the like refer to relatively high, relatively low,etc.

In accordance with some embodiments, a computing device may filtermeasurements having labels occurring with less prominence and/or lessconformance than other labels. Filtering, which may be implementeddifferently for different scale levels, as described further herein, maypermit display and/or visualization of a level of detail appropriate fora particular layer, for example. In one possible example, a researchermay wish to view an epidemiological map on a national scale. Tofacilitate viewing at a relatively higher scale level, for example,details that may be useful at a relatively lower level scale level, suchas details at a town or village level, for example, may be merged by wayof spatial filtering and/or one or more image processing techniques, sothat nuances of relatively larger scale phenomena (e.g., intrusion of anepidemic into neighboring countries) that may be of interest may bepresented more effectively.

In a particular implementation, a Gaussian smoothing operation may beemployed at levels other than a relatively high resolution. For example,in an embodiment, measurements providing a highest resolution for aparticular set of available measurements may be referred to as a‘zeroth’ layer or level. At various subsequent layers, in an embodiment,a Gaussian smoothing function may comprise a kernel, which may, forexample, be scaled proportional to a square root of a layer numericalvalue if the layer value comprises a nonzero value. In someimplementations, while smoothing a first layer, a scale factor “σ” maybe used, wherein a corresponds to a standard deviation of the particularGaussian kernel at the particular level or layer. Accordingly, at afirst layer, σ may be scaled to approximately equal 1.0. At a secondlayer, a may be scaled to approximately equal to the square root of 2.0,or approximately 1.414. Scaling of a to successively higher relativevalues to perform smoothing operations at layers other than the zerothlayer may, at least in some implementations, result in relatively finescale features being reduced, such as at least approximatelymonotonically, as subsequent layers (e.g., first, second, third, and soforth) are constructed. For example, in an embodiment, roughlyequivalent or consistent intervals may be employed between immediatelysuccessive layers or levels. Although Gaussian smoothing may becontemplated for at least some implementations, as was indicated, othertypes of smoothing and/or convolution approaches, such as binomial,Savitsky-Golay, and so forth, may be utilized and are included withinclaimed subject matter. However, use of other types of smoothing and/orconvolution approaches may result, at least in part, in an introductionof fine scale features (artifacts) as the values after smoothing and/orconvolution may underflow, or overflow, for example.

FIGS. 1A-1C are diagrams showing regions within a geographical areaidentified by labeled measurements according to an embodiment. In FIG.1A, map 10 represents the North American continent comprising identifiedregions 100, 150, and 175, wherein a scale of 1.0 cm representsapproximately 1000.0 km. Identified region 100 may correspond to, forexample, the San Joaquin Valley in California of the western UnitedStates. Identified region 150 may correspond to, for example, a portionof the Sonoran Desert in the southwest portion of the United States.Identified region 175 may correspond to, for example, an area of thecentral plains of the United States.

In one non-limiting example, identified regions 100, 150, and 175 mayencompass locations within which a weather event, such as flooding,drought, etc., may be occurring. Accordingly, for the case of a drought,for example, measurements, such as reports of river levels at a giventime, underground aquifer levels, mean daytime temperatures at differentlocations and times, and/or other indicators from locations withinidentified regions 100, 150, and 175 may be assigned a labelcorresponding to “drought,” for example. However, claimed subject matteris, of course, not limited to the use of particular geographicidentified regions, particular weather occurrences within identifiedregions, particular times, or particular labels assigned tomeasurements, which are provided as illustrations.

In FIG. 1B, a map 11 showing identified region 100′, which maycorrespond, for example, to the San Joaquin Valley in California, may beseen with greater detail than identified region 100 of FIG. 1A. In FIG.1B, 1.0 cm represents approximately 300.0 km. Identified regions 112,114, 116, and 118, which are not visible in identified region 100 ofFIG. 1, are visible in FIG. 1B. For the example of FIG. 1B, measurementsreported from locations within identified regions 112, 114, 116, and 118may, for example, describe river levels, underground aquifer levels,and/or other indicators. Labeled measurements associated with locationswithin these identified regions may be assigned a label corresponding to“severe drought,” for example.

In FIG. 1C, identified regions 112′, 114′, 116′, and 118′ of a map 12are visible, for example. In FIG. 1C, 1.0 cm represents approximately150.0 km. Within identified region 112′ of FIG. 1C, identified region113 is visible. Within identified region 114′, identified region 115 isvisible. Within identified region 116′, identified region 117 isvisible. Within identified region 118′, identified region 119 isvisible. For the example of FIG. 1C, measurements from locations withinidentified regions 113, 115, 117, and 119 may, for example, describeriver levels, underground aquifer levels, and/or other indicators.Measurement labels corresponding to locations from within theseidentified regions may be assigned a label corresponding to “extremedrought,” for example.

From FIGS. 1A, 1B, and 1C and as described herein, visible regionsidentified by “drought,” “severe drought,” and/or “extreme drought,” forexample, for example, may be displayed at a level of resolutionconsistent with a given scale and/or zoom command (or pan command) froman interface, such as a graphical user interface (GUI). Additionally,identified regions 100, 100′, and 100″ are shown with increasing detailas resolution increases. For example, identified region 100, as shown inFIG. 1A, may appear to be smoothed in relation to identified region 100′of FIG. 1B, as a result of increased smoothing of histograms used toform region 100, for example. Additionally, identified region 100′ ofFIG. 1B may appear to be smoothed in relation to identified region 100″of FIG. 1C as a result of increased smoothing of histograms used to formregion 100′, for example. Likewise, identified regions 112, 114, 116,and 118 of FIG. 1B may appear smoothed in relation to identified regions112′, 114′, 116′, and 118′, of FIG. 1C, as a result of smoothing ofhistograms representing labeled measurements. In the example of FIGS.1A-1C, a level of detail appropriate for the particular layer (e.g., 1.0cm=1000.0 km, for example) may be achieved by smoothing a relativelyhigher-resolution layer (e.g. 1.0 cm equals 300.0 km), for example.

One or more identified regions displayed in greater detail in arelatively higher resolution layer need not be completely or evenpartially contiguous to be displayed, as such for a lower resolutionlayer. For example, in FIG. 10, identified region 126 is shown asdetached from identified region 116′. Similarly, identified region 127is shown as detached from identified region 100″. However, identifiedregions 126 and 127 may be incorporated into larger immediately adjacentregions in FIG. 1B, for example. These examples illustrate, for anembodiment, use of one or more image processing techniques, which mayinclude morphological transforms, such as contour tracing, closing,filling, dilation, erosion, or combinations thereof. Image processingtechniques may be employed with relatively higher resolution regions inorder to display corresponding regions at relatively lower resolutionsusing an appropriate level of detail, in an embodiment.

It may be seen from FIGS. 1A, 1B, and 10 that in response to an increasein a scale level, for example, labeled measurements may be omitted. Forexample, changing a scale level from a level represented in FIG. 1C to alevel represented in FIG. 1B, regions characterized by labeledmeasurements such as “extreme drought,” for example (e.g., regions 113,117, 115, and 119) may be omitted. Likewise, changing a scale level fromscale a represented in FIG. 1B to a scale represented in FIG. 1A,regions characterized by labeled measurements such as “severe drought,”for example (e.g., regions 112, 114, 116, and 118) may be omitted.

FIG. 2 is a flow diagram of a process for forming one or more regionscharacterized by labeled measurements according to an embodiment 20.Computing environment 40 of FIG. 5 may be suitable for performing themethod of embodiment 20. However, claimed subject matter is not limitedto the particular implementation of FIG. 5 and alternate arrangements ofcomponents in other implementations may be used. Example embodiments,such as embodiment 20 shown in FIG. 2 and others herein, may includeblocks in addition to those shown and described, fewer blocks, blocksoccurring in an order different than may be identified, or anycombination thereof.

In FIG. 2, labeled measurements in the form of captured still images ofpopular attractions in New York City are shown as comprising inputsignals to process embodiment 20. Although three captured images areillustrated, other examples may include labeled reports, labeled eventnotices, labeled video, labeled audio, labeled multimedia clips, and/orother encoded materials, and claimed subject matter is not limited inthis regard. In implementations, labeled measurements represented by205, 210, and 215 may number in the thousands or millions (or more) andclaimed subject matter is intended to embrace any number of measurementswithout limitation. In at least one implementation, labeled measurementsmay be associated with relatively small regions, widespread regions,and/or regions that may encompass the entire surface of the Earth orotherwise. However, claimed subject matter is not limited to physicalregions on the surface of the Earth. Furthermore, implementations maymake use of hundreds, thousands, millions or even more labeled capturedimages. Further, labeled measurements representing event notices,reports, video clips, audio clips, multimedia segments, and so forth,for example, may also be represented by input signals to processembodiment 20. That is, captured images, as here, are provided as anon-limiting example illustration.

Captured images 210, 215, and 220 have been assigned Label_(—)1 andLabel_(—)2. Captured images 210, 215, and 220 may be accessed, forexample, from a database and/or other repository. For example, labeledmeasurements may represent images uploaded by many thousands or evenmillions of individuals capturing the image, a name corresponding to theattraction, (e.g., Chrysler building, United Nations building, Statue ofLiberty, and so forth), name of an event, and/or any other label, asexamples, and claimed subject matter is not limited in this respect. Forthe purposes of discussion of FIG. 2, Label_(—)1 may be assumed to betext, such as “New York” or, for example, a slightly misspelled version,such as “Niew York.”

As shown in FIG. 2, captured images 210, 215, and 220 may additionallybe associated with a measurement corresponding to, for example, alocation at which the image was captured. For the example of FIG. 2,location may also correspond to latitude and/or longitude at which aphotographic image was captured. However, locations may be expressed inother forms, such as street addresses, well-known establishments, suchas Yankee Stadium, or any other technique of expressing a location in areasonably unambiguous manner, and claimed subject matter is not limitedin this respect.

For process embodiment 20 of FIG. 2 at 225, normalization and filteringof labels assigned to captured images may take place. For example, aslight misspelling of “New York,” such as “Niew York,” may be corrected.In implementations, normalization may comprise other operations such astranslation from one language to another, as may be encountered if oneor more captured images is assigned a label, for example, “Park” (inEnglish), “Parc” (in French), and “Parque” (in Spanish). In certainimplementations, normalization may also involve removal of diacriticalmarks, for example, present in a first language but not present in asecond language. For example, measurements assigned a labelcorresponding to “Espana” (in Spanish) may be normalized to “Spain” inEnglish. It should be noted that many types of translations from a firstlanguage to a second language are possible, as well as transliterationfrom a first character set to a second character set may be possible ofbeing performed at 225, and claimed subject matter is intended toembrace all such occurrences or embodiments.

In particular implementations, given a set of labels, denoted as “Λ,” ameasurement collection may be described as D_(A)=∪ of D|λεΛ, wherein the“∪” symbol indicates a union of various labeled measurements, such asassociated with a label D_(λ), for example. In an implementation,measurements associated with multiple labels, such as Label_(—)1,Label_(—)2, and so forth, may be included as many times in D_(A) forwhich a particular asset has labels. For example, if a measurement isassigned 15 labels, (e.g., Λ=15) for example, a possibility for 15 labelnormalization operations may be performed. In an embodiment, it may benoted that any number of labels may be assigned to a measurement, andclaimed subject matter is not limited in this respect. As it pertains toa location associated with captured images, such as 210, 215, and 220,there is further described as D_(λ)≈{d}, in which “d” comprises a label“λ” and may be represented by an ordered list of elements, such as(l_(h), l_(w)) which may contain a geographic location expressed bylongitude l_(h) and latitude l_(w).

Mathematically, a density histogram may be expressed in one possibleexample, a discretized histogram, as, for a label, such as Label_(—)1,for captured images, substantially in accordance with the followingexpression:

$\begin{matrix}{{D_{\lambda}} = {\sum\limits_{x = 1}^{w}{\sum\limits_{y = 1}^{h}{f_{\lambda}\left( {w,h} \right)}}}} & (1)\end{matrix}$

Wherein, expression 1 uses D_(λ), for Label_(—)1, Label_(—)2, and soforth, producing in this example a two-dimensional density histogramf_(y)(w,h). In at least one implementation, locations associated withlabeled measurements may be represented using discrete increments oflongitude and latitude. Accordingly, for increments of 0.01 degreeslatitude, 36,000 discrete increments (w) are possible(360°/0.01°=36,000). Additionally, for increment sizes of 0.01 degreeslongitude, 18,000 discrete increments (h) are possible(180°/0.01°=18,000). In the histogram illustrated at 230, grid pointsare shown at intersections of lines of constant “h” and lines ofconstant “w,” for example.

At 225, label measurements may also be filtered according to variouscriteria as later described. In particular implementations, filteringmay be performed to detect, for example, those labels for a particularcountry, area, sub-region, locality, or other geographical extent.Filtering may be performed to distinguish among labeled measurementsassociated with a particular country, for example, a particularlandmark, a particular naturally occurring feature, etc., and claimedsubject matter is intended to embrace all types of filtering andcategorization of labels, such as those described herein.

To form a histogram, at grid points, such as illustrated at 240, whichmay number 36,000 (approximately) in a horizontal direction (w) and maynumber 18,000 (approximately) in a vertical direction (h), a linearscale-space representation of a family of increasingly smooth histogramsL_(λ)(w,h;t) for a given label (λ) may be described or computed as aconvolution of f_(λ)(w,h) with Gaussian kernels represented by G(w,h;t)may be described substantially in accord with expression 2, below as:

L _(λ)(w,h;t)=G(w,h;*t)*f _(λ)(w,h)  (2)

Wherein, t denotes variance of a kernel, and operator “*” indicates aconvolution operation. As alluded to above, in process embodiment 20kernel operations may be calculated and/or scaled so that Gaussiansmoothing may be performed.

For example, a bin of the illustrated histogram may be seen as varyingin size. In an implementation, variance in histogram bin size mayrepresent variations in measurement density for a nearby grid point. Inone possible example, a larger bin may represent a correspondingly largenumber of labeled measurements near a popular attraction of New York,(e.g., Statue of Liberty). In this instance, on a relative basis, alarger bin may indicate that a large number of labeled measurementscorresponding to images captured at the Statue of Liberty may berepresented as input signals to process embodiment 20.

In an illustrated histogram of 240, smaller bins, for example, mayindicate that on a relative basis a lesser number of labeledmeasurements may be represented as input signals to process embodiment20. For example, it may be reasonable to assume that somewhat fewerimages may be captured at less popular locations within New York City,such as at locations along the banks of the East River. Likewise, binsof a histogram that appear to be blank or unpopulated may be indicativeof locations at that do not correspond to locations at which anycaptured images are represented as inputs signals to process embodiment20. It should be noted, however, that in other implementations,histogram bins represented at 240 may be constructed differently, suchas by way of a grayscale, wherein darker shades of gray indicate higherdensity, and lighter shades of gray indicate lower density, as anexample.

Likewise, 240 of FIG. 2 may include use of a spatial bandpass filterthat serves to filter or to at least limit a contribution of spatialfrequencies that may be inappropriate for a level of scale for anidentified region being displayed. In this context, the term spatialfrequency refers to a spatial domain measure of irregularities and/orundulation in a number occurrences. For example, in FIG. 1A, it can beseen that relatively fine details present at, for example, FIG. 1B orFIG. 1C are removed or omitted. Thus, for example, in the event that aresearcher is interested in high-level trends, such as the occurrence ofdroughts across the continental United States, it may be appreciatedthat details presented pertain more to relatively high-level contoursrather than to relatively fine details. Likewise, a researcher studyinga particular drought in the San Joaquin Valley of California (FIG. 1B),may be interested in identified regions encompassed by a drought, andmay be less interested in those locations within the San Joaquin Valleycorresponding to measurements labeled with “severe drought,” forexample.

In mathematical terms, at least for an implementation, spatial filteringmay be expressed in expression 4 substantially as follows:

$\begin{matrix}{{{Filter}\mspace{14mu} {output}} = \left\{ \begin{matrix}1 & {{{{if}\mspace{14mu} {L_{\lambda}\left( {w,{h;t}} \right)}} - {L_{\lambda}\left( {w,{h;{t + a}}} \right)}} > ɛ} \\0 & {otherwise}\end{matrix} \right.} & (4)\end{matrix}$

Wherein L_(λ)(w, h; t) expresses occurrence of measurements associatedwith a particular label at a particular grid point (w, h) for a layer“t.” L_(λ)(w, h; t+a) expresses occurrence of measurements associatedwith a particular label at a particular point (w, h), at a slightlyhigher layer of t+a. A residual “ε” indicates a measure of “visualnoise” and/or “jitter” that is to be tolerated in measurements typicallyto account for statistical variation.

In an implementation, selection of variable “a” used in a difference(e.g., bandpass filtering) operation, such as expression 4 may approachzero. In this instance, expression 4 may closely approximate theLaplacian of the Gaussian, which may effectively detect edges betweentwo areas of relatively uniform, but different, intensities in ahistogram, such as histogram 242 and/or 244, although, likewise, othermethods may also be used. In some instances, variable “a” may comprise asomewhat larger numeral, and claimed subject matter is intended toembrace implementations in which “a” may approach zero as well as thosefor which “a” may approximate larger values. In implementations, forexample, layers t=4.0, t=16.0, t=64.0 and so forth may be used. Variable“a” may comprise values such as 5.0, 10.0, 15.0, or larger and/or may beexpressed as a function of t, for example.

At 240, for an embodiment, one or more image processing techniques mayalso be used to form boundaries of identified regions characterized bylabeled measurements. In an implementation, use of image processingtechniques corresponding to morphological transforms, such as filling,closing, contour tracing, dilation, erosion, and/or other operations,either alone or in combination, may provide visually meaningful shapingas well as removal of details that may not be appropriate or consistentwith a given layer. In one example, returning briefly to FIGS. 1A-1C,identified 112, 114, 116, and 118, visible in FIG. 1B, and which spanand/or encompass regions on the order of 100.0 km, are not visible inidentified region 100 of FIG. 1A, for which 100.0 km may beapproximately equal 1/10 of a centimeter. Thus, while viewing FIG. 1A,which may represent a layer corresponding to a scale having relativelylow resolution among FIGS. 1A-1C, a user need not be distracted byrelatively fine details that may be relatively uninteresting if viewedat a layer showing a continent, for example.

Image processing techniques, such as morphological filling, which may,for example, emphasize spatial extent of a label measure rather thandensity, may be achieved by contour tracing a binary representation toextract outer contours that may mark or designate areas where a labelmeasure, such as conformance and/or prominence, for example, asdiscussed, may drop below a threshold, for example. If outer contours ofan identified region may be discerned, areas enclosed by outer contoursmay be filled, such as by identified region 100, as in FIG. 1A.Likewise, identified regions 113, 117, 115, and 119, for example, arevisible in FIG. 1C, but are filled by surrounding identified regions112′, 116′, 114′, and 118′, respectively, in FIG. 1B. However,operations and/or processing may also be achieved through othertechniques, and claimed subject matter is not limited in this respect.

Image processing techniques described herein may also includemorphological closing. For example, returning to FIG. 1C, identifiedregions 126 may be seen as slightly detached from identified region116′. Similarly, identified region 127 may be seen as slightly detachedfrom identified region 100″. In at least certain implementations, detailmay be of a reduced relative signal value while identified region 100may be viewed with relatively lower resolution, such as in FIG. 1B, toshow identified region 100′. To remove identified regions 126 and 127,morphological closing may be employed in which disjointed or detachedfeatures are separated from an identified region by a small gap, forexample, so as to close the gap. Morphological closing may beimplemented in an embodiment by performing a dilation operation followedby an erosion operation, for example. In a dilation and/or erosionoperation, a disk-shaped structuring element “S” may be used to form abinary representation of an identified region. Mathematically, this maybe expressed substantially in accordance with expression 5 below:

b(w,h;t)·S=(b(w,h;t)⊕S)⊖S  (5)

wherein b(w,h;t) of expression 5 denotes a binary representation of anidentified region, and wherein “S” denotes a disk-shaped structuringelement having a radius “ρ” selected substantially according to adesired smoothing, as illustrated in FIG. 3. In the example of FIG. 3,disk 310 having radius p may be selected substantially according to aparticular scale, as influenced by a particular layer, such as t=2.0,t=3.0, and so forth. In an implementation, for layers in which a greaterlevel of detail is desired on a relative basis, such as layer t=1.0, forexample, a disk of a smaller radius “ρ” may be appropriate, resulting inless smoothing of an outer contour on a relative basis. For layers inwhich a lesser relative level of detail is desired, such as layer t=5.0,a disk of a relatively larger radius “ρ” may be appropriate, resultingin greater relative smoothing of an outer contour.

FIGS. 4A and 4B show additional image processing techniques that may beused to assist in forming an identified region according to anembodiment. In FIG. 4A, embodiment 40 shows identified region 350 in adilated state, which may reduce relative detail in outer contours.Dilation of identified region 350 results, at least in part, in dilatedidentified region 350′. In FIG. 4B, dilated identified region 350′ iseroded to form identified region 350″, comprising much less relativedetail than identified region 350 shown in FIG. 4A.

Image processing techniques referenced in descriptions of FIGS. 4A, and4B represent just a few of many possible techniques that may be utilizedto identify regions from a binary representation of an identifiedregion, such as given by b(w,h;t) of expression 5, in an embodiment. Forexample, a morphological closing operation may be performed by one ormore operations, such as described with reference to FIGS. 4A, and 4B,for example. In another example, morphological filling, such aspreviously described herein, may be substituted for, or performed inaddition to, a flood filling operation, that, for example, may skipinner (nested) contours, such as identified regions 113, 117, 115, and119 of FIG. 1C. It is contemplated that claimed subject matter embracesany of a host of possible image-processing techniques, such as, forexample, technique to process contours of identified region boundaries.

Scale-space theory, for example, which refers to a framework formulti-scale signal representation, may be employed in processing labeledmeasurements associated with a location. At 230, a space or other regionof interest may be divided into sub-regions. In one possible example, aregion, such as region 232, which may represent a portion of a countrythat may encompass approximately 2.0° in longitude and 2.0° inlongitude, may be divided into approximately 100 sub-regions, such assub-regions 234 and 236. It should be noted that claimed subject matteris not limited to division of any particular region size, any particularnumber of sub-regions, and/or any particular size of a sub-region. Inimplementations, dividing a region into a number of sub-regions mayenable parallel processing of underlying data used to create regionsusing, for example, a Gaussian smoothing kernel or other type ofoperation, such as a transform or transformation operation, for example,that may be used for image processing. In at least one implementation,image processing may involve use of an approximately uniform Gaussiansmoothing kernel. A Gaussian smoothing kernel may be used to “blur”histograms representing labeled measurements within sub-regions, forexample. Gaussian blurring may enable histograms to be formed intosub-regions having discernible contours. Sub-regions corresponding tovarious geographical sub-regions on the surface of the Earth, forexample, may be joined or aggregated to form regions representinglabeled measurements. However, claimed subject matter is not limited toforming sub-regions and regions representing geographical locations orto image processing. In other possible examples, labeled measurementsmay represent measurements from any domain, for example, useful tocharacterize physical measurements.

At 240, histograms for sub-regions may be constructed. In an embodiment,histograms representing labeled measurements for sub-regions, such assub-regions of approximately 0.2° longitude and approximately 0.2°latitude may be constructed. Labeled measurements may be distributedwithin histograms corresponding to, for example, sub-regions, such assub-regions 234 and 236. Histograms may be constructed to represent adensity of labeled measurements corresponding to, for example, nearbygrid points.

In a particular implementation, for example, histograms 242 and 244 mayrepresent two example sub-regions corresponding to sub-regions of region232. Although many more sub-regions corresponding to region 232 arepossible, histogram 242, comprising a number of horizontal grid points,w_(n), and comprising a number of vertical grid points, h_(n), areillustrated in FIG. 2. Also illustrated is histogram 244, whichcomprises a number of horizontal grid points, w_(m), and vertical gridpoints, h_(m). In one possible example, grid points may represent binsor other groupings of labeled measurements comprising a size of interestof approximately 0.005° longitude and 0.005° latitude. It iscontemplated that histogram bins may number into the hundreds,thousands, or may comprise a greater number depending on, for example,size of a region being divided, a number of sub-regions, etc., andclaimed subject matter is not limited in these respects.

In an implementation, for a layer of relatively higher resolution,computation of kernel functions for Gaussian smoothing, such as at 235,might not be performed and smoothing might not be used. Thus, whiledisplaying a distribution of locations corresponding to labeledmeasurements, for example, an unfiltered or “raw” distribution ofdiscretized locations may be displayed. Accordingly, at 240, it may beseen that for a layer t=0.0, at least in the facet shown in FIG. 2,discrete points of varying sizes may be displayed. Discrete points ofvarying sizes may, for example, indicate variations in density across achosen axis, such as the x-axis (e.g. latitude) or the y-axis (e.g.,longitude), for example. It should be noted that in otherimplementations, some level of smoothing may occur at layer t=0.0, andclaimed subject matter is not limited in this respect.

In an embodiment, at subsequent layers, for example, based at least inpart on calculated kernel functions, effects of Gaussian smoothingacross a chosen axis (such as longitude and/or latitude) may begenerated at 240. At a layer t=1.0, discretized locations of relativelyhigh density may be represented as a line segment that may result from asmoothing process, for example. At layer t=2.0, that a line segmentpresent at layer t=1.0 may be replaced by more gradual transitionsand/or softer curves for an embodiment. Likewise, at a layer t=n,gradual transitions and/or softer curves shown at layer t=2.0 may bereplaced by even more gradual transitions, reflecting an increased levelof Gaussian smoothing that may occur as scale is increased, such as foran embodiment. However, again, it should be noted that contours merelydescribe one illustrative example implementation, such as Gaussiansmoothing in a single dimension, for longitude or latitude. For example,an embodiment may include many dimensions, as previously mentioned.

In evaluating effects of smoothing at 240, similarities between theprevious discussion and FIGS. 1A-1C may be apparent. At layer t=1.0, forexample, relatively fine features of identified regions may bediscernible. Thus, layer t=1.0 may correspond, at least in someimplementations, to a one-dimensional portion or “slice” of FIG. 1C, inwhich relative fine features of identified region 100″ may bedistinguished, at a scale of 1.0 cm equals approximately 150.0 km, forexample. Layer t=1.0 may thus correspond, at least in one possibleexample, to an identified region displaying locations of labeledmeasurements in relatively fine detail. However, again, FIGS. 1A-1C aremerely illustrative, and claimed subject matter is not limited in thisrespect.

At layer t=2.0 of block 240, for example, less relatively fine featuresof identified regions may be discernible. Thus, layer t=2.0 maycorrespond, at least in some implementations, to a one dimensionalportion of FIG. 1B, in which at least some features of identified region100′ may be distinguished, at a scale of 1.0 cm equals approximately300.0 km. Likewise, at layer t=n shown at 240, for example, few featuresof identified regions may be discernible. Thus, layer t=n may thuscorrespond, at least in an example, to an identified region displayinglocations of labeled measurements in relatively less detail, such asillustrated by region 100 of FIG. 1A, for example.

Returning to FIG. 2, at 245 a characteristic, such as conformance and/orprominence, for example, of at least one of Label_(—)1 and Label_(—)2may be determined. In implementations, detecting conformance, forexample, of a first label, such as Label_(—)1, relative to a secondlabel, such as Label_(—)2, may permit selective display of certainregions identified by particular uniformity and/or Conformance oflabeled measurements. In one possible example, to illustrate, a tourismofficial may be interested in determining how many visitors to New YorkCity actually capture images at the Statue of Liberty versus those whocapture images at the East River. To enable such an investigation,captured images corresponding to locations within New York City may, forexample, be assigned Label_(—)1=“Statue of Liberty.” Other capturedimages, for example, may be assigned Label_(—)2=“East River.”Accordingly, by comparing conformance of the two labels (e.g., “Statueof Liberty” vs. “East River”) among a number of labeled measurements mayprovide insights to such an investigation. Further, displayingidentified regions characterized by locations corresponding to capturedimages may, at least in part, in an embodiment, for example, allowvisualization of identified regions associated with conforming and/orprominent labels, for example. It should be noted, however, that this ismerely one example of a myriad of examples in which labels may beassigned to measurements to give rise to useful insights, such as usingcharacteristics, like conformance and/or prominence among labels, as anexample. Claimed subject matter is not limited in this respect.

At 245, a label's characteristic value measure, such as conformanceand/or prominence, in relation to a threshold level may be determined.In implementations, determining a label's characteristic value in such afashion, as an example, may provide, at least in part, a basis forcontrasting certain labeled measurements from other labeledmeasurements, for example. In one possible example, a tourism officialmay infer that many more tourists visited New York City by detecting anincrease in a percentage of labeled measurements comprising capturedimages associated with a label “Statue of Liberty,” if, for example, apercentage of such labels, compared with a total number of labeledcaptured images, is greater than a threshold. However, a similarincrease in a percentage of captured images associated with a label“East River,” may be less relevant to an increase in tourists if apercentage of such labels, if compared with compared with a total numberof labeled captured images, is less than a threshold. To illustrate, ifan approximately 20.0% increase in labeled images having the label“Statue of Liberty” (e.g., 50,000 to 60,000 images drawn from a set of100,000 images) is computed, such an increase may possibly indicate asignificant increase in visitors touring New York City. However,detection of a 20% increase in images having a label “East River” (e.g.,500 to 600 images drawn from a set of 100,000 images) may be less likelyto be indicative of an increase in visitors to New York City.

In some implementations, a measurement label's conformance andprominence may be compared. In some implementations, it may be useful toemphasize a contribution of conforming labels in relation to a setcomprising a larger group labels. For example, if 15 labels from a setof 100 labels are substantially identical, this may be expressed as aconformance parameter of 15/100. However, it may be useful toadditionally employ a “ρprominence” parameter, as an enhancement of aconformance parameter, in an embodiment. In one example, a prominenceparameter may be described in mathematical terms, substantially inaccordance with expression 3, below

$\begin{matrix}{L_{\lambda}^{\prime} = \frac{\left( {L_{\lambda}\left( {w,{h;t}} \right)} \right)^{2}}{L_{\Lambda}\left( {w,{h;t}} \right)}} & (3)\end{matrix}$

Wherein, prominence for a label measure at a particular grid point of ahistogram, for example, is given by L′_(λ). L_(λ)(w,h;t) expressesincidence of a particular label at a particular grid point, andL_(Λ)(w,h;t) expresses a measure density at a particular grid pointacross labels, at least in one implementation. Of course, variable “t”represents a particular layer at which prominence is to be determined.In one possible example, a conformance parameter of 15/100 may beconverted, in accordance with expression 3, for example, to a prominenceparameter of 2.25. Accordingly, in one example, a 1.0% increase in thepresence of a label, such as from 15.0% to 16.0%, may result in anincrease in prominence of 2.25 to 2.56.

At 250, perhaps responsive to a user selecting to change a scale atwhich a region may be displayed, for example, histograms may be reducedin size. In certain implementations, responsive to a user selecting todecrease a relative zoom level (e.g., display a relatively largergeographical region at a relatively lower resolution) a size of interestof histograms, such as histograms 242 and 244, may be increased on arelative basis. In at least one implementation, a size of interest ofhistograms may be increased, for example, by combining label densitiesassociated with grid points. In an implementation, combining labeldensities associated with grid points may comprise, for example,averaging histogram bins of adjacent grid points. In otherimplementations, combining may comprise summing, detecting anapproximate maximum of adjoining histogram bins, detecting anapproximate minimum of histogram bins, randomly choosing a histogrambin, approximately always choosing a histogram bin value in a particular“corner” (e.g., southwest, southeast, northwest, northeast) of adjoininghistogram bins, or combining by way of other guidelines. Claimed subjectmatter is intended to embrace all forms of combining of adjacenthistogram bins

In FIG. 2, for example at 252, a size of interest comprising labeledmeasurements associated with four grid points arranged at 0.001°increments on the surface of the Earth, (e.g., southwest, southeast,northwest, and northeast of a grid point) may be rescaled to compriselabeled measurements associated with grid points arranged at 0.002°increments, such as size of interest 254. In an implementation, aportion of histograms 242 and 244 may be rescaled to relatively decreasehistogram size by a factor of four, for example. In an implementation, auser selecting to decrease a relative zoom level by an additionalamount, a size of interest on a relative basis comprising labeledmeasurements may be further relatively scaled such as, for example, gridpoints arranged at 0.002° to grid points arranged at 0.004°.

In implementations, a smoothing kernel, such as a Gaussian smoothingkernel, for example, may remain approximately constant as size ofinterest is increased and/or decreased. In particular implementations,use of a similar-sized smoothing kernel at varying scale levels maysimplify computational complexity. For example, without significantlyreducing size one or more histograms, as in 250, for example, anincrementally larger Gaussian smoothing kernel may be employed. However,increasing smoothing kernel size may also otherwise lead to a relativeincrease in computational complexity. For example, processing, such asconvolution, of a histogram size of interest using a 3×3 Gaussian kernelsize may use, for example, approximately nine multiplication operationsand one division operation for a given histogram bin. However,convolving a histogram size of interest using a 5×5 Gaussian kernel sizemay use, for example, approximately 25 operations and one divisionoperation for a given histogram bin. Accordingly, it may be appreciatedthat use of a smaller Gaussian kernel, may, for example, reducecomputational complexity on an approximately inverse square basis. Inother embodiments, use of a smaller Gaussian kernel may reducecomputational complexity by other factors, for example.

It may also be appreciated that if immediately adjacent histogram binsare combined, such as at 250 of FIG. 2, some loss in histogram fidelitymay occur. However, in practice, at least in some implementations,combining of immediately adjacent histogram sizes of interest, from, forexample, a size of interest of 0.001° in latitude and longitude to, forexample 0.002° in latitude and longitude, may not significantly distortor bring about significant loss of fidelity of a histogram.Additionally, at least in some implementations, advantages brought aboutby reductions in computational complexity may outweigh disadvantagespotentially introduced by combining immediately adjacent histogram sizesof interest.

At 260, responsive to relative rescaling a sub-region, labelcharacteristic measures, such as conformance and/or prominence, forexample, at a rescaled sub-region may be determined. Accordingly, 260may comprise determining influence of labels of a rescaled region. 260may include omitting measured labels associated with less conformingand/or prominent labels. In implementations, omitting of measured labelsassociated with less conforming and/or prominent labels may comprisesubtracting a scaled or rescaled histogram version for a sub-region froma histogram for a sub-region obtained prior to scaling or rescaling.

In one example, obtaining a difference of Gaussians between immediatelysuccessive levels of histograms formed at varying scale levels, such assubtracting a scaled or rescaled histogram from a constructed histogram,may at least in part, allow features of particular levels to emerge.Conformance and/or prominence of labels associated with measurements maybe determined on a per-layer basis in an embodiment, for example. In animplementation, detecting conformance and/or prominence on a per-layerbasis may, in an embodiment, allow visualization of identified regionsassociated with particular labels at levels of scale appropriate to alayer on a relative basis and allow discarding measured labelsassociated with less conforming and/or less prominent labels, ifdesired. For example, a tourism official interested in determininginterests of visitors of New York City may wish to study labelconformance and/or prominence of labeled measurements on a city level,and may not be interested in tourism happenings at a state level.Accordingly, at a city level, labels, such as, for example, “Statue ofLiberty” or other attractions may be of particular interest. In anotherexample, a tourism official interested in determining interests ofvisitors to New York State, may, for example, be interested in labelconformance of measurements at the state level, where prominent labelsas “Poconos Mountains,” or “Finger Lakes,” may be emphasized whiledeemphasizing and/or discarding measurements having less prominentlabels. However, other implementations may determine conformance and/orprominence based at least in part on criteria other than by a per layerbasis, and claimed subject matter is not limited in this respect.Rather, this is merely one illustrative example.

FIGS. 3A and 3B show a joining and infilling operation that may be usedto form one or more regions characterized by labeled measurementsaccording to an embodiment 30. In FIG. 3A, example smoothed histogram310, having a vertical dimension h_(n) and a horizontal dimension w_(n),and smoothed histogram 315, having a vertical dimension h_(n) andhorizontal dimension w_(m), are shown. Histogram smoothing may be atleast partially in response to a smoothing operation using a Gaussiankernel, or may be in response to use of other image processingoperations. As shown in FIG. 3A, a smoothing operation may result in aportion of smoothed histogram 310 overlapping a histogram boundary, asshown by overlapping portion 320. Likewise, a smoothing operation mayresult in a portion of smoothed histogram 315 overlapping a histogramboundary, as shown by overlapping portion 325.

In FIG. 3A, notch 327 may be seen at a location along an outer contourof overlapping portion 320 of smoothed histogram 310, and extending intoa histogram border. In implementations, histogram smoothing may giverise to contour features, such as notch 327, at one or more locations atwhich, for example, a value of a characteristic of a label (e.g.,conformance, prominence, etc.) may drop below a threshold at leastapproximately. Smoothing of histogram 315 may also give rise to contourfeatures, such as those of overlapping portion 325. However, as shown inFIG. 3A, Gaussian smoothing or other type of image processing mayresult, at least in part, in a smoothed portion that may not comprisesuch a feature. In implementations, presence or absence of an irregularfeature of a contour, such as notch 327, may arise from computationalround-off and/or truncation errors that may occur during convolution ofa Gaussian kernel used in processing a histogram.

FIG. 3B, shows smoothed histograms generating sub-regions may be joinedto form larger, closed regions. For example, smoothed histograms 310 and315 may be joined to form histogram 330, as shown in embodiment 31. Inan example, an aggregated histogram, having a vertical dimension h_(n)and a horizontal dimension W_(n+m) may be formed by joining smoothedhistograms 310 and 315. Additional histograms (not shown in FIG. 3B) maylikewise be joined to form closed regions characterized by labeledmeasurements, for example. In particular implementations, joininghistograms may bring about infilling of a portion of a first sub-region,such as notch 327 of smoothed histogram 310. In certain implementations,additional image processing operations may be conducted, such as regiondilation and/or erosion, which may reduce detail in outer contours.

In an implementation, a prominence parameter may be computedsubstantially according to expression 3, wherein a particular label,such as L_(λ)(w, h; t) is squared (e.g., (L_(λ)(w, h; t))²) to emphasizeits contribution in relation to occurrence of other labels in a givenlayer (t), such as L_(Λ)(w,h;t). Although expression 3 indicates that anemphasis, such as prominence, may be computed using a square ofcontribution of a particular label, operations other than squaring thenumerator of expression 3 may be performed in other computations tononetheless provide a numerical measure of emphasis. For example, inlieu of squaring the numerator, the numerator may be raised to a powerof, for example, 1.5. In other implementations, exponents greater than2.0 may be employed, such as 2.1, 2.5, and so forth. Further, in someimplementations, variations of expression 3 may be employed in which thenumerator of expression 3 need not be exponentiated. Claimed subjectmatter is intended to embrace any mathematical operation to obtainemphasis of a first label relative to a second label, for example.

In an implementation, conformance and/or prominence may be used toidentify sub-regions within identified regions having particular levelsof conformance and/or prominence, for example. For example, a firstiso-contour may identify a sub-region within which a conformanceparameter of at least 15/100 with respect to a particular label may bepresent. A second iso-contour, for example, may identify a sub-regionwithin which a conformance parameter of at least 25/100 with respect toa particular label may be present. Likewise, iso-contours may identifysub-regions within which prominence parameters with respect to aparticular label, such as greater than 2.25, greater than 2.56, and soforth, may be present.

For purposes of illustration, FIG. 5 is an illustration of an embodimentof a computing platform or computing device 410 that may be employed ina client-server type interaction, such as described infra. In FIG. 5, aserver may interface with a client 400 or 410, which may comprisefeatures of a conventional client device, for example. Communicationsinterface 420, processor (e.g., processing unit) 450, and memory 470,which may comprise primary memory 474 and secondary memory 476, maycommunicate by way of communication bus 440, for example. In FIG. 5,client 410 may store various forms of content, such as analog,uncompressed digital, lossless compressed digital, or lossy compresseddigital formats for content of various types, such as video, imaging,text, audio, etc. in the form physical states or signals, for example.Client 410 may communicate with a server by way of an Internetconnection via network 415, for example. Although the computing platformof FIG. 4 shows the above-identified components, claimed subject matteris not limited to computing platforms having only these components asother implementations may include alternative arrangements that maycomprise additional components, fewer components, or components thatfunction differently while achieving similar results. Rather, examplesare provided merely as illustrations. It is not intended that claimedsubject matter to limited in scope to illustrative examples.

Processor 450 may be representative of one or more circuits, such asdigital circuits, to perform at least a portion of a computing procedureor process. By way of example but not limitation, processor 450 maycomprise one or more processors, such as controllers, microprocessors,microcontrollers, application specific integrated circuits, digitalsignal processors, programmable logic devices, field programmable gatearrays, and the like, or any combination thereof. In implementations,processor 450 may perform signal processing to manipulate signals orstates or to construct signals or states, for example.

Memory 470 may be representative of any storage mechanism. Memory 470may comprise, for example, primary memory 474 and secondary memory 476,additional memory circuits, mechanisms, or combinations thereof may beused. Memory 470 may comprise, for example, random access memory, readonly memory, or one or more data storage devices or systems, such as,for example, a disk drive, an optical disc drive, a tape drive, asolid-state memory drive, just to name a few examples. Memory 470 may beutilized to store a program, as an example. Memory 470 may also comprisea memory controller for accessing computer readable-medium 480 that maycarry and/or make accessible content, code, and/or instructions, forexample, executable by processor 450 or some other controller orprocessor capable of executing instructions, for example.

Under the direction of processor 450, memory, such as cells storingphysical states, representing for example, a program, may be executed byprocessor 450 and generated signals may be transmitted via a network,such as the Internet, for example. Processor 450 may also receivedigitally-encoded signals from server 400.

Network 415 may comprise one or more communication links, processes,and/or resources to support exchanging communication signals between aclient and server, which may, for example, comprise one or more servers(not shown). By way of example, but not limitation, network 415 maycomprise wireless and/or wired communication links, telephone ortelecommunications systems, Wi-Fi networks, Wi-MAX networks, theInternet, the web, a local area network (LAN), a wide area network(WAN), or any combination thereof.

The term “computing platform,” as used herein, refers to a system and/ora device, such as a computing device, that includes a capability toprocess and/or store data in the form of signals and/or states. Thus, acomputing platform, in this context, may comprise hardware, software,firmware, or any combination thereof (other than software per se).Computing platform 410, as depicted in FIG. 5, is merely one suchexample, and the scope of claimed subject matter is not limited to thisparticular example. For one or more embodiments, a computing platformmay comprise any of a wide range of digital electronic devices,including, but not limited to, personal desktop or notebook computers,high-definition televisions, digital versatile disc (DVD) players and/orrecorders, game consoles, satellite television receivers, cellulartelephones, personal digital assistants, mobile audio and/or videoplayback and/or recording devices, or any combination of the above.Further, unless specifically stated otherwise, a process as describedherein, with reference to flow diagrams and/or otherwise, may also beexecuted and/or affected, in whole or in part, by a computing platform.

Memory 470 may store cookies relating to one or more users and maycomprise a computer-readable medium that may carry and/or makeaccessible content, code and/or instructions, for example, executable byprocessor 450 or some other controller or processor capable of executinginstructions, for example. A user may make use of an input device, suchas a computer mouse, stylus, track ball, keyboard, or any other devicecapable of receiving an input from a user.

Regarding aspects related to a communications or computing network, awireless network may couple client devices with a network. A wirelessnetwork may employ stand-alone ad-hoc networks, mesh networks, WirelessLAN (WLAN) networks, cellular networks, or the like. A wireless networkmay further include a system of terminals, gateways, routers, or thelike coupled by wireless radio links, and/or the like, which may movefreely, randomly or organize themselves arbitrarily, such that networktopology may change, at times even rapidly. Wireless network may furtheremploy a plurality of network access technologies, including Long TermEvolution (LTE), WLAN, Wireless Router (WR) mesh, or 2nd, 3rd, or 4thgeneration (2G, 3G, or 4G) cellular technology, or other technologies,or the like. Network access technologies may enable wide area coveragefor devices, such as client devices with varying degrees of mobility,for example.

A network may enable radio frequency or wireless type communications viaa network access technology, such as Global System for Mobilecommunication (GSM), Universal Mobile Telecommunications System (UMTS),General Packet Radio Services (GPRS), Enhanced Data GSM Environment(EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced, Wideband CodeDivision Multiple Access (WCDMA), Bluetooth, 802.11b/g/n, or other, orthe like. A wireless network may include virtually any type of nowknown, or to be developed, wireless communication mechanism by whichsignals may be communicated between devices, such as a client device ora computing device, between or within a network, or the like.

Communications between a computing device and a wireless network may bein accordance with known, or to be developed cellular telephonecommunication network protocols including, for example, global systemfor mobile communications (GSM), enhanced data rate for GSM evolution(EDGE), and worldwide interoperability for microwave access (WiMAX). Acomputing device may also have a subscriber identity module (SIM) card,which, for example, may comprise a detachable smart card that storessubscription information of a user, and may also store a contact list ofthe user. A user may own the computing device or may otherwise be itsprimary user, for example. A computing device may be assigned an addressby a wireless or wired telephony network operator, or an InternetService Provider (ISP). For example, an address may comprise a domesticor international telephone number, an Internet Protocol (IP) address,and/or one or more other identifiers. In other embodiments, acommunication network may be embodied as a wired network, wirelessnetwork, or combination thereof.

A computing device may vary in terms of capabilities or features.Claimed subject matter is intended to cover a wide range of potentialvariations. For example, a network device may include a numeric keypador other display of limited functionality, such as a monochrome liquidcrystal display (LCD) for displaying text. In contrast, however, asanother example, a web-enabled computing device may include a physicalor a virtual keyboard, mass storage, one or more accelerometers, one ormore gyroscopes, global positioning system (GPS) or otherlocation-identifying type capability, and/or a display with a higherdegree of functionality, such as a touch-sensitive color 2D or 3Ddisplay, for example.

A computing device may include or may execute a variety of now known, orto be developed operating systems, or derivatives and/or versions,including personal computer operating systems, such as a Windows, iOS orLinux, or a mobile operating system, such as iOS, Android, or WindowsMobile, or the like. A computing device may include or may execute avariety of possible applications, such as a client software applicationenabling communication with other devices, such as communicating one ormore messages, such as via email, short message service (SMS), ormultimedia message service (MMS), including via a network, such as asocial network including, but not limited to, Facebook, LinkedIn,Twitter, Flickr, or Google+, to provide only a few examples. A computingdevice may also include or execute a software application to communicatecontent, such as, for example, textual content, multimedia content, orthe like. A computing device may also include or execute a softwareapplication to perform a variety of possible tasks, such as browsing,searching, playing various forms of content, including locally stored orstreamed video, or games such as, but not limited to, fantasy sportsleagues. The foregoing is provided merely to illustrate that claimedsubject matter is intended to include a wide range of possible featuresor capabilities.

A network including a computing device, for example, may also beextended to another device communicating as part of another network,such as via a virtual private network (VPN). To support a VPN,transmissions may be forwarded to the VPN device. For example, asoftware tunnel may be created. Tunneled traffic may, or may not beencrypted, and a tunneling protocol may be substantially complaint withor substantially compatible with any past, present or future versions ofany of the following protocols: IPSec, Transport Layer Security,Datagram Transport Layer Security, Microsoft Point-to-Point Encryption,Microsoft's Secure Socket Tunneling Protocol, Multipath Virtual PrivateNetwork, Secure Shell VPN, or another existing protocol, or anotherprotocol that may be developed.

A network may be compatible with now known, or to be developed, past,present, or future versions of any, but not limited to the followingnetwork protocol stacks: ARCNET, AppleTalk, ATM, Bluetooth, DECnet,Ethernet, FDDI, Frame Relay, HIPPI, IEEE 1394, IEEE 802.11, IEEE-488,Internet Protocol Suite, IPX, Myrinet, OSI Protocol Suite, QsNet,RS-232, SPX, System Network Architecture, Token Ring, USB, or X.25. Anetwork may employ, for example, TCP/IP, UDP, DECnet, NetBEUI, IPX,Appletalk, other, or the like. Versions of the Internet Protocol (IP)may include IPv4, IPv6, other, and/or the like.

It will, of course, be understood that, although particular embodimentswill be described, claimed subject matter is not limited in scope to aparticular embodiment or implementation. For example, one embodiment maybe in hardware, such as implemented to operate on a device orcombination of devices, for example, whereas another embodiment may bein software. Likewise, an embodiment may be implemented in firmware, oras any combination of hardware, software, and/or firmware, for example(other than software per se). Likewise, although claimed subject matteris not limited in scope in this respect, one embodiment may comprise oneor more articles, such as a storage medium or storage media. Storagemedia, such as, one or more CD-ROMs and/or disks, for example, may havestored thereon instructions, executable by a system, such as a computersystem, computing platform, or other system, for example, that mayresult in an embodiment of a method in accordance with claimed subjectmatter being executed, such as a previously described embodiment, forexample; although, of course, claimed subject matter is not limited topreviously described embodiments. As one potential example, a computingplatform may include one or more processing units or processors, one ormore devices capable of inputting/outputting, such as a display, akeyboard and/or a mouse, and/or one or more memories, such as staticrandom access memory, dynamic random access memory, flash memory, and/ora hard drive.

In the preceding detailed description, numerous specific details havebeen set forth to provide a thorough understanding of claimed subjectmatter. However, it will be understood by those skilled in the art thatclaimed subject matter may be practiced without these specific details.In other instances, methods and/or apparatuses that would be known byone of ordinary skill have not been described in detail so as not toobscure claimed subject matter. Some portions of the preceding detaileddescription have been presented in terms of logic, algorithms, and/orsymbolic representations of operations on binary signals or states, suchas stored within a memory of a specific apparatus or special purposecomputing device or platform. In the context of this particularspecification, the term specific apparatus or the like includes ageneral purpose computing device, such as general purpose computer, onceit is programmed to perform particular functions pursuant toinstructions from program software.

Algorithmic descriptions and/or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processingand/or related arts to convey the substance of their work to othersskilled in the art. An algorithm is here, and generally, is consideredto be a self-consistent sequence of operations and/or similar signalprocessing leading to a desired result. In this context, operationsand/or processing involve physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical and/or magnetic signals and/or states capable of beingstored, transferred, combined, compared, processed or otherwisemanipulated as electronic signals and/or states representinginformation. It has proven convenient at times, principally for reasonsof common usage, to refer to such signals and/or states as bits, data,values, elements, symbols, characters, terms, numbers, numerals,information, and/or the like. It should be understood, however, that allof these or similar terms are to be associated with appropriate physicalquantities and are merely convenient labels. Unless specifically statedotherwise, as apparent from the following discussion, it is appreciatedthat throughout this specification discussions utilizing terms such as“processing,” “computing,” “calculating,” “determining”, “establishing”,“obtaining”, “identifying”, “selecting”, “generating”, and/or the likemay refer to actions and/or processes of a specific apparatus, such as aspecial purpose computer and/or a similar special purpose computingdevice. In the context of this specification, therefore, a specialpurpose computer and/or a similar special purpose computing device iscapable of processing, manipulating and/or transforming signals and/orstates, typically represented as physical electronic and/or magneticquantities within memories, registers, and/or other information storagedevices, transmission devices, and/or display devices of the specialpurpose computer and/or similar special purpose computing device. In thecontext of this particular patent application, as mentioned, the term“specific apparatus” may include a general purpose computing device,such as a general purpose computer, once it is programmed to performparticular functions pursuant to instructions from program software.

In some circumstances, operation of a memory device, such as a change instate from a binary one to a binary zero or vice-versa, for example, maycomprise a transformation, such as a physical transformation. Withparticular types of memory devices, such a physical transformation maycomprise a physical transformation of an article to a different state orthing. For example, but without limitation, for some types of memorydevices, a change in state may involve an accumulation and/or storage ofcharge or a release of stored charge. Likewise, in other memory devices,a change of state may comprise a physical change, such as atransformation in magnetic orientation and/or a physical change ortransformation in molecular structure, such as from crystalline toamorphous or vice-versa. In still other memory devices, a change inphysical state may involve quantum mechanical phenomena, such as,superposition, entanglement, and/or the like, which may involve quantumbits (qubits), for example. The foregoing is not intended to be anexhaustive list of all examples in which a change in state form a binaryone to a binary zero or vice-versa in a memory device may comprise atransformation, such as a physical transformation. Rather, the foregoingis intended as illustrative examples.

While there has been illustrated and/or described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the relevant art that various other modifications may be madeand/or equivalents may be substituted, without departing from claimedsubject matter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from one or more central concept(s) described herein.Therefore, it is intended that claimed subject matter not be limited tothe particular examples disclosed, but that such claimed subject mattermay also include all aspects falling within appended claims and/orequivalents thereof.

1. A method comprising: computing, in parallel, a plurality ofsub-regions formed from one or more smoothed overlapping histogramsrepresenting labeled measurements, said sub-regions in combinationrepresenting a closed region; wherein said computing is performed atvarying scale levels employing a similar-sized smoothing kernel at saidvarying scale levels by initially scaling said labeled measurements to asize of interest at a particular scale level before employing saidsimilar-sized smoothing kernel.
 2. The method of claim 1, wherein saidsize of interest corresponds to a size of a bin in a two-dimensionalhistogram.
 3. The method of claim 2, said computing further comprisescombining immediately adjacent histogram bins of said size of interestto obtain a different size of interest.
 4. The method of claim 1,further comprising: omitting labeled measurement density outside ofselected borders of said smoothed overlapping sub-regions.
 5. The methodof claim 4, further comprising: joining said plurality of overlappingsub-regions to form said closed region.
 6. The method of claim 5,wherein said joining further comprises: infilling a portion of a firstsub-region of said plurality of smoothed overlapping sub-regionsresulting, at least in part, from smoothing one or more histogramsrepresenting a second sub-region of said plurality of smoothedoverlapping sub-regions.
 7. The method of claim 1, wherein saidcomputing includes: constructing at least two histograms representinglabel density of said labeled measurements for two successive of saidvarying scale levels
 8. The method of claim 7, wherein a first of saidat least two histograms comprises approximately an inverse square of thegrid points that comprise a second of said at least two histograms priorto said scaling.
 9. The method of claim 8, wherein said inverse squarecomprise at least one of the following: one-quarter; one-ninth; orone-twenty-fifth.
 10. The method of claim 8, wherein said computingcomprises obtaining a difference of Gaussian using differently sizedGaussian kernels to said first histogram.
 11. An apparatus, comprising:one or more processing units to: scale, at varying scale levels, labeledmeasurements to a size of interest; smooth, in parallel, one or morehistograms representing a plurality of sub-regions characterized bylabeled measurements to form a plurality of smoothed overlappingsub-regions, wherein one or more histograms representing said smoothedoverlapping sub-regions are aggregated to form a closed regioncharacterized by at least one label of said labeled measurements. 12.The apparatus of claim 11, wherein said one or more processing units isadditionally to: form histograms having bins corresponding to a size ofinterest within said sub-regions.
 13. The apparatus of claim 12, whereinsaid one or more processing units is additionally to: form a pluralityof combined histogram bins representing labeled measurements fromlabeled measurements of adjacent histogram bins.
 14. The apparatus ofclaim 12, wherein said one or more processing units is additionally to:determine conformance of at least one label of said labeled measurementsof said closed region characterized by at least one label of saidlabeled measurements.
 15. The apparatus of claim 11, wherein said one ormore processing units is additionally to: determine a difference ofGaussian among aggregated sub-regions to determine spatial frequenciesamong at least two of said aggregated sub-regions.
 16. An articlecomprising: a non-transitory storage medium comprising machine-readableinstructions stored thereon which are executable by a special purposecomputing apparatus to: scale, at varying scale levels, labeledmeasurements to a size of interest; employ similar-sized smoothingkernels to sub-regions in a labeled measurement space, said sub-regionsrepresenting portions of a closed region characterized by a particularlabeled measurement.
 17. The article of claim 16, wherein saidnon-transitory storage medium additionally comprises machine-readableinstructions stored thereon which are executable by said special purposecomputing apparatus to: form a histogram representing a distribution oflabeled measurements having a particular label.
 18. The article of claim17, wherein said non-transitory storage medium additionally comprisesmachine-readable instructions stored thereon which are executable bysaid special purpose computing apparatus to: join adjacent histogrambins to obtain said size of interest.
 19. The article of claim 18,wherein said non-transitory storage medium additionally comprisesmachine-readable instructions stored thereon which are executable bysaid special purpose computing apparatus to: obtain said size ofinterest by joining four adjacent histogram bins into a single histogrambin.
 20. The article of claim 16, wherein said non-transitory storagemedium additionally comprises machine-readable instructions storedthereon which are executable by said special purpose computing apparatusto: infill a portion of a first sub-region as a result of smoothing oneor more histograms representing said first and a second sub-region.